On the other hand, there is also a surge of studies on decentralized algorithms that run independently on each player, requiring only local information such as the player's own action and the corresponding reward feedback [1]}, [2]}, [3]}, [4]}, [5]}.
Compared to centralized ones, decentralized algorithms are usually more versatile and can potentially run in different environments (cooperative or competitive).
Many of them enjoy the property of being rational (as defined in [6]}), in the sense that a player's policy converges to the best response to the opponent no matter what stationary policy the opponent uses.
However, it is also often more challenging to show the convergence to a Nash equilibrium when the two players execute the same decentralized algorithm.
